Organizations As Information
Processing Systems
Office of Naval Research
Technical Report Series
An Exploratory Analysis of the 4
Relationship Between Media Richness
and Managerial Information Processing
Robert H. Lengel ;
Richard L. Daft I
TR-0NR-DG-08 ,
i
July 1984 j |
i
I
Department of Management
Texas A&M University
i''34
A
Richard Daft
and
Ricky Griffin
Principal Investigators
!»
An Exploratory Analysis of the
Relationship Between Media Richness
and Managerial Information Processing
Robert H. Lengel
Richard L. Daft
i, I Ai.l* t. iiiU'l . 1*lJ-
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An l-.xpl oratory Analysis of the Relationship
between Media Richness and Managerial information
Processing
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t ajthcro;
Robert II. Lengel
Richard I.. Daft
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t >>f III OHminO oho AMI J A T ION name and address
College of Business Administration
Texas A&M University
College Station, TX 77843
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AREA 4 WORK UNIT NUMBERS
NR 170-950
1 1 CONTROLLING OFFICE NAME AND ADDRESS
Organizational Effectiveness Research Programs
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June 1984
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19 KEY WORDS (Continue on nm» otdo II nocoooory «»V Identity by block nuwifror;
Information
Information Processing • Communication i
Information Richness Organizational Communication
Managerial Information Processing Organizational Information Processing
i
20 ABSTRACT fConlJrtu* on rtvwi* side If no c«a>vy ond Idondty by block number) i
A dilemma exists between technical inf ormat-ion- designers and students of i
managerial information behavior. A richness model is proposed that uses j
the concepts of media richness and communication learning requirements to J
integrate the two perspectives. The concepts and model were tested in a J
four-stage research program, and they were generally supported. Managers j
tended to prefer rich, oral media when learning requirements were high I
and less rich, written media when learning reciulrements were low. j
_ - - ■ ■ ' . _ . >
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Office of Naval Research
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NR 170-950
AN EXPLORATORY ANALYSIS OF THE RELATIONSHIP BETWEEN MEDIA RICHNESS AND
MANAGERIAL INFORMATION PROCESSING
Robert H. Lengel and Richard L. Daft
Co-Principal Investigators
Department of Management
College of Business Administration
Texas A&M University
College Station, TX 77843
TR-ONR-DG-01
TR-ONR-DG-02
TR-0NR-DG-03
TR-0NR-DG-04
TR-ONR-DG-05
TR-0NR-DG-06
Joe Thomas and Ricky W. Griffin.
The Social Information Processing Model of Task Design:
A Review of the Literature. February 1983.
Richard L. Daft and Robert M. Lengel.
Information Richness: A New Approach to Managerial
Behavior and Organization Design. May 1983.
Ricky W. Griffin, Thomas S. Bateman, and James
Skivington. Social Cues as Information Sources:
Extensions and Refinements. September 1983.
Richard L. Daft and Karl E. Weick.
Toward a Model of Organizations as Interpretation
Systems. September 1983.
Thomas S. Bateman, Ricky W. Griffin, and David
Rubenstein. Social Information Processing and
Group-Induced Response Shifts. January 1984.
Richard L. Daft and Norman B. Macintosh.
The Nature and Use of Formal Control Systems for
Management Control and Strategy Implementation. February
1984.
TR-0NR-DG-07 Thomas Head, Ricky W. Griffin, and Thomas S. Bateman.
Media Selection for the Delivery of Good and Bad News: A
Laboratory Experiment. May 1984.
Robert H. Lengel and Richard L. Daft.
An Exploratory Analysis of the Relationship Between Media
Richness and Managerial Information Processing. July
1984.
TR-0NR-DG-08
»
AM EXPLORATORY ANALYSIS OF THE RELATIONSHIP BETWEEN
MEDIA RICHNESS AMD MANAGERIAL INFORMATION PROCESSING
Abstract
A dilemma exists between technical information designers and students
of managerial information behavior. A richness model is proposed that uses
the concepts of media richness and communication learning requirements to
integrate the two perspectives. The concepts and model were tested in a
four-stage research program, and they were generally supported. Managers
tended to prefer rich, oral media when learning requirements were high and
less rich, written media when learning requirements were low.
>
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I
AN EXPLORATORY ANALYSIS OF THE RELATIONSHIP BETWEEN
MEDIA RICHNESS AND MANAGERIAL INFORMATION PROCESSING
Information is the life-blood of organizations. Participants,
especially managers, exchange information to interpret the external
environment, coordinate activities, resolve disagreements, establish goals
and targets, make technical and administrative decisions, and disseminate
rules and instructions (Arrow, 1974; Porter and Roberts, 1976; Tushman and
Nadler, 1978; Galbraith, 1973). Managers spend the majority of their time
interacting with other people, and additional time is spent with mail,
reports, and printouts (Mintzberg, 1972). The importance of information is
reflected in the technology available to make information processing more
efficient (Conrath and Bair, 1974; Parsons, 1983; Harris, 1980; Gerstein
and Reisman, 1982). Micro-computers, word processors, teleconferencing,
electronic mail, and database management techniques are adopted by
organizations on the premise that more efficient information processing
will mean a more efficient organization.
Feldman and March (1981) proposed that the study of information in
organizations involves a dialectic between students of information behavior
and information engineers. The engineering (or technical) approach to
information emphasizes precision, clarity, logic, and cost-benefit ratios.
Information engineers use technology to design optimal information systems
that will provide clear, correct data to help managers solve current
problems (Keen, 1977; Henderson and Nutt, 1978). Students of information
behavior often focus on the social, intuitive, and seemingly non-logical
aspects of information processing in organizations. Students of this
social perspective observe actual information encounters and try to make
sense of them.
The technical and social perspectives represent an unresolved dilemma
for the study of information processing. Each perspective explains a
limited aspect of managerial behavior; neither perspective reconciles the
view of the other. Consider, for example, the following observations.
1. Managers seem to prefer oral means of communication. Managers
spend little time thinking, planning, writing, or using the formal means of
information at their disposal (Mintzberg, 1973; Kurke and Aldrich, 1983).
Decision making often involves gossip, unofficial data, informal
communication, and intuition. Managers move toward live action, away from
thoughtful reflection, toward personal contact, and away from formal
reports and data.
2. The mode of presentation influences the impact of information on
the receiver. Case illustrations and verbal stories seem to have greater
impact than hard statistical data on people’s judgement (Borgada and
Nisbett, 1977; McArthur, 1972, 1976; Martin and Powers, 1980a, 1980b;
Nisbett and Ross, 1980). O'Reilly (1980) concluded that humans are
influenced more by vivid, concrete examples than by dry statistics, even
though statistics present better systematic evidence from multiple
observations.
3. The role of information and decision support systems in
organizations seems limited (Mitroff and Mason, 1983). After great initial
optimism, the credibility of operations research/management science data
gathering and decision techniques has weakened, even while an increasing
number of managers have received formal training in these techniques
(Ackoff, 1976; Dearden, 1972; Larson, 1974; Grayson, 1973; and Levitt,
1975). Although information hardware and technologies have become more
powerful and sophisticated, the outputs apparently are not used more for
decision making at upper management levels.
4. Organizational learning and adaptation often seem threatened by
the very systems designed to scan the environment and provide information
displays to managers. The formal systems, once in place, may hamper search
and filter away change signals, even when the organization is in a changing
environment (Hedberg and Jonsson, 1978; Mowshowitz, 1976; Hedberg, 1981;
Hedberg, Nystrom, and Starbuck, 1976). Technology based probes and
forecasting mechanisms become part of the programmed behavior and defined
structure of the organization. They apparently foster stability and
inertia rather than the learning and adaptation these probes and mechanisms
are supposed to facilitate.
These observations about managerial information behavior illustrate
the dilemma. Why do managers prefer face-to-face exchanges of information
in lieu of expensive and extensive computer based management aids, or
written media in general? Why does soft information often have more impact
than hard data? Why do scanning systems promote inertia rather than
learning? The literature does not provide good answers. Tushman and
Nadler (1978) concluded that technology oriented information designers lack
a theory of managerial information needs because designers are motivated to
find ways to fit data to hardware. Students of social information
behavior, on the other hand, find their observations difficult to formulate
into an operational model because of the complexity of the social context.
Both technological and social sources of information are present in
organizations, and these sources are used at certain times for certain
things (Huber, 1982; O'Reilly, 1982). A logical next step in the
development of a theory of information behavior would be to reconcile the
formal, written information modes with the informal and face-to-face.
The dialectic associated with managerial information behavior is the
puzzlement that motivated the research reported in this paper. The purpose
of this paper is to propose and test a model to partially integrate the
exposing viewpoints. We define "media richness" and "translation
requirements" as concepts that can be used to explain managerial
information behavior. Media richness reflects the capacity to convey
information between managers, and we propose that media are selected based
on manager information requirements. By exploring managerial communication
preferences in terms of a new theoretical framework, we will try to find an
initial answer to the dialectic on information processing within
organizations.
Theory Development
Information and Learning
One underlying purpose of human communication is mutual learning.
Learning in organizations is a process of gaining knowledge or
comprehension of organization reality (Hedberg, 1981), especially knowledge
of action-outcome relationships (Duncan and Weiss, 1979) and organizational
errors (Argyris, 1976). It seems clear that organizations, or rather their
human participants, must be capable of learning from their environments if
they are to survive and be effective. Participants need to acquire and
share some minimum understanding of their organizational world, of what to
do, of how and when to do it. Learning involves the processing of
information.
The definition of information typically includes the concepts of
uncertainty, utility, and relevance (Shannon and Weaver, 1949; Garner,
1962; MacKay, 1969; Helvey, 1971). Human beings represent what they know
by mental images, pictures, symbols, and verbal statements. When managers
process cues that make some change in their mental representation, and
thereby reduce uncertainty or increase utility for the problem at hand,
then information processing has occurred. Data, by contrast, are the input
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and output of any communication channel (MacKay, 1969). Managers work in a
sea of data that is only potential information. If managers consume this
data with some purpose or intent in mind, their mental pictures may be
changed. Data thus becomes information when it is perceived, when it has
relevance and utility for managers, and thereby facilitates learning.
The information-data distinction is one step toward the resolution of
the technical and social information perspectives. Managerial information
processing is an outcome not directly visible to observers or researchers
(Gifford, Bobbitt and Slocum, 1979). Only managers know if data provides
utility, changes their mental representation, and facilitates learning.
Data flow, by contrast, is observable and amenable to technology. Data can
be counted in the form of letters, words, number of reports, and telephone
calls. Managers may use just a fraction of the data available to them to
make sense of a complex, changing social system. Managers appear to
process data continuously, but the actual learning event is related to the
use of information inside the manager's mind.
Translation Requirements
Data becomes information if learning occurs. The amount of learning
required in an organizational communication is reflected in the amount of
change in mental representation required to achieve mutual understanding.
We propose that the difficulty or ease of attaining mutual understanding is
related to message content and the similarity in frame of reference of the
sender and receiver.
A person'3 frame of reference is formed from a combination of
cognitive elements, organizational role, previous experience, and other
personal characteristics (Lawrence and Lorsch, 1967; Shrivastava and
Mitroff, 1984). Communication becomes more difficult as the experience of
individuals diverges and as the subjective or equivocal (Weick, 1979)
-6-
oontent of a message increases. A person trained as a scientist may have a
difficult time understanding the point of view of a lawyer. Emotion-laden
messages often are personal and subjective, and therefore open to
misinterpretation. In these cases a common perspective does not exist and
information processing is required before understanding can occur.
Messages are complex, equivocal, and difficult to interpret. Learning
requirements are high.
On the other hand, if the perspectives of managers are similar, the
task of reaching mutual understanding is easier. Similarity in the
experience or background of the sender and receiver as well as objective,
unequivocal content in the message reduces the need for changes in mental
representation (Daft and Macintosh, 1981). In these cases a common view of
the situation already exists and serves to facilitate the interpretation of
the message. For example, if one scientist communicates with another
scientist on a routine technical matter, there will be a high degree of
confidence that the message will be understood without elaboration. Mutual
understanding is relatively easy to achieve. Learning requirements are
small .
The amount of learning required between sender and receiver is a
c~itical element in information processing. The process of overcoming
differences in perspectives to achieve a common understanding will be
called "translation." Translation is defined as the extent of change or
conversion required in perspective between sender and receiver to attain
mutual understanding. The concept of translation is useful because it can
serve as an operational surrogate for managerial learning requirements. We
propose that the amount of translation required in a communication
transaction is an underlying force that drives managerial communication
behavior. Learning requirements determine the usefulness of information
-7-
►
sources and provide a potential explanation for why managers prefer various
forms of communication.
Media Richness
The translation requirement in a communication episode reflects the
amount of learning necessary to achieve mutual understanding. We propose
that managers select media to accommodate translation needs. Communication
media available to managers (e.g., telephone, computer printout,
face-to-face conversation) differ in their ability to facilitate learning.
Media influence the capacity to process information among managers.
The role of media becomes clearer if by looking at one information
carrier that media utilize, which is language. Daft and Wiginton (1979)
proposed that languages can be arrayed along a continuum of language
variety. The continuum captures the intuitive idea that languages differ
in their ability to convey meaning. Numbers, for example, convey greater
precision of meaning than do poems or pieces of abstract art. Many human
values and feelings are so complex and equivocal that they do not lend
themselves to precise, quantitative descriptions. Conversely, the use of
music or art to describe the physical relationship between force, mass and
acceleration is not as effective as using simple, precise equations.
According to Daft and Wiginton, effective description occurs when language
variety matches the amount of uncertainty or equivocality in the concept to
be transmitted.
The concept of language variety suggests that the mode of
communication needs to be adjusted to fit the topic to be communicated.
Language variety, however, is only one aspect of managerial communication.
We propose the broader concept of media richness to explain the selection
of media by managers to process information. Media richness is defined as
a medium's capacity to process information. Richness is the relative
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ability of information to influence or change mental representations and
thereby to facilitate learning (Lengel, 1983; Daft and Lengel, 1984).
Bodensteiner (1970) proposed the concept of a media hierarchy, ranking
media channels in terms of their mechanical characteristics for processing
different types of information. Bodensteiner ' s model incorporated four
media classifications — face-to-face, telephone, addressed documents, and
unaddressed documents. These media and the basis for proposed differences
in richness are shown in Figure 1. The richness of each medium is based on
four criteria: (1) the use of feedback so that errors can be corrected;
(2) the tailoring of messages to personal circumstances; (3) the ability to
convey multiple information cues simultaneously; and (4) language variety.
[Figure 1 about here]
Face-to-face is hypothesized to be the richest information medium.
Face-to-face communications allow immediate feedback so that understanding
can be checked and misinterpretations corrected if the message is complex
or equivocal. This medium also allows the simultaneous communication of
multiple cues, including body language, facial expression, and tone of
voice, which convey information beyond the spoken message (Meherabian,
1971). Face-to-face information also is of a personal nature and utilizes
high variety natural language.
The telephone medium is somewhat less rich than face-to-face.
Feedback capability is fast, but visual cues are not available.
Individuals have to rely on language content and audio cues to reach
understanding, although the medium is personal and does utilize high
variety language.
Written communications are still lower in media richness. Feedback is
slow. Only data written down are conveyed, so visual cues are limited to
those on paper. Although audio cues are absent, natural language can be
<
-9-
utilized. Addressed documents can be tailored to the individual recipient,
and thus are of a personal nature and are somewhat richer than standard
documents or bulletins.
Formal, unaddressed documents are lowest in media richness. One
example would be quantitative reports from a computer. These
communications often utilize numbers, which are useful in communicating
simple, quantifiable aspects of organizations, but do not have the
information carrying capacity of natural language (Daft and Wiginton,
1979). Another example would be a standard flier or bulletin issued to all
managers in the organization. This medium is low in richness because these
documents provide no opportunity for visual cues, feedback, or
personalization.
The media richness hierarchy shown in Figure 1 is simple, but it helps
organize ideas from the information literature. For example, the
difference between oral and written communication is illustrated in the
hierarchy. Face-to-face and telephone communications are richer than
written communications, which may explain why top managers prefer oral
media (Mintzberg, 1972). Oral communications provide immediate feedback,
high variety language, a variety of cues and personal tailoring that make
them a powerful means of conveying information. Another example is
management information systems. Most information system reports go in the
category of unaddressed documents, and thus are low in richness. Other
research has been concerned with information sources such as human versus
documentary (Keegan, 197*0 > personal versus impersonal (Aguilar, 1967), and
such things as files, formal reports, or group discussions (O'Reilly, 1982;
Kafalas, 1975). The media richness continuum helps explain these
differences. Each medium is not just a source, but a complex act of
information processing. Each medium is unique in terms of feedback, cues,
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and language variety — all of which influence learning between sender and
receiver.
Richness Model
The proposed model of managerial information processing is presented
in Figure 2. The Figure 2 model hypothesizes a positive relationship
between media richness and the translation requirements in communication
transactions. Our reasoning is that managers will select a rich medium
when the message is difficult and learning requirements are high. A rich
medium provides a mechanism for managers to learn and achieve mutual
understanding when perspectives diverge and message content is subjective
and difficult. Information processing must resolve inherent equivocality
sufficient to capture different perspectives. Learning is facilitated by
rich media. Less rich media are appropriate when perspectives are similar
and the learning requirement is low. Media low in richness provide an
efficient way to communicate an objective, unequivocal message to others.
[Figure 2 about here]
The richness match in Figure 2 provides a way to explain managerial
information processing. It departs from the engineering metaphor of
precision and clarity as the desired information state for managers.
Precision and clarity are important, but when the communication task is
objective and the mutual learning requirement is small. A richness
mismatch may explain failures to transfer understanding. Written media and
standard MIS reports may oversimplify complex problems, because these media
do not transmit the subtleties associated with the unpredictable, personal,
subjective aspects of organizations. On the other hand, the model in
Figure 2 suggests that face-to-face media should not be matched to
objective, well-understood communication transactions. For simple
messages, face-to-face discussion may contain surplus meaning. Multiple
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cues may not always agree — facial expression may distract from spoken
words. Multiple cues can overcomplicate the communication and distract the
receiver's attention from the routine message.
The organizational literature lends support to the Figure 2 model,
although the support is indirect because managerial information activities
have not been conceptualized along a richness hierarchy. For example,
Mintzberg (1973) observed that chief executive officers display a strong
preference for oral media. Top management issues are difficult, personal,
intangible, and require the integration of diverse views and perspectives
(Daft and Lengel, 1983). Top managers thus relied on rich media to process
information to facilitate learning about high translation issues.
Research examining the relationship between task uncertainty and
information processing also support the model. Van de Ven, Delbecq, and
Koenig (1976) studied task uncertainty and coordination modes. Under
conditions of high ta3k uncertainty (high learning requirements), managers
preferred face-to-face modes of coordination. When task uncertainty was
low, rules and procedures were used, which are lower in richness. Meissner
(1969) and Randolph (1978) found that when communications were objective
and certain, less personal sources of information such as objects, signs,
signals, and written documents were used. Personal (face-to-face) means of
communication were used more frequently as tasks increased in uncertainty.
Holland, Stead, and Leibrock (1976) gathered questionnaire data from
R&D units, and found that personal channels of communication were important
when perceived uncertainty was high. They concluded that face-to-face
communications enabled participants to learn about complex topics in a
shorter time. Written information sources, such as the professional
literature and technical manuals, were preferred when task assignments were
well understood.
The research into management information systems shows a similar
pattern. Higgins and Finn (1977) examined top management attitudes toward
management information systems, and found that intuitive judgment was used
more often than computer analysis in strategic decisions. Brown (1966)
argued that decision support systems have greater value for technical
problems. Management information systems are more relevant to managers who
work with well-defined operational decisions (Blandin and Brown, 1977).
Management information systems represent media that are low in richness,
and are suited to information tasks that have a small translation
component.
The basic proposition to be tested in this research is that
organizational information processing is characterized by a match between
the information media selected by managers and the extent of mutual
learning required to reach understanding. This relationship is summarized
in the following hypothesis.
Hypothesis 1: Managerial information processing
patterns will be characterized by a positive relationship
between the richness of media selected and the translation
requirements of communication episodes.
As an auxiliary hypothesis, we also propose that learning requirements
explain the selection of oral versus written media as described by Mintzberg
(1973). The predicted relationship is summarized in the following hypothesis.
Hypothesis la: Managers will select oral media for
high translation communication episodes and written media
for low translation communication episodes.
Moderating Influences. The above discussion argues for a positive
relationship between media richness and message translation requi -ements .
However, other factors may moderate manager media selection patterns.
Communication activities may be influenced by the experience and personality
of the manager, and by the sender versus receiver role in the communication
transaction. Even if the model is supported in terms of the relationship in
hypothesis one, the personality and role of respondents may moderate this
relationship.
Previous research has shown variation in information processing behavior
associated with the personality traits of communication propensity (Dance,
1967) and extroversion versus introversion (Daft, 1978). Other personality
characteristics — tolerance for ambiguity (Budner, 1962; Dermer, 1973)
cognitive complexity (Downey and Slocum, 1975; Stabell, 1978), and incongruity
adaptation level (HunsaKer, 1973) — have been indirectly associated with
communication through the respondent's interpretation of perceived information
complexity. Propensity to communicate and introvert-extrovert traits,
however, are related to one another and to information behavior (Carskadon,
1979; Dance, 1967; Daft, 1978). Extroverts tend to initiate communications
and to enjoy personal interactions. If an individual is an extrovert, he or
she could bias media selection in the direction of increased richness, that
is, extroverts may have a greater preference for personal media such as
face-to-face and telephone. Introverts may prefer to avoid face-to-face
contact in favor of impersonal media such as notes, memos, or bulletins.
Introverts differ from extroverts by their preference to be alone and to have
fewer personal contacts. We thus hypothesize that personality of the
respondent may influence media selection as follows:
Hypothesis 2: Managers classified as extroverts will,
on the average, select richer media to accomplish
communication transactions than will managers classified as
introverts.
The other moderating factor pertains to a possible difference between
senders versus receivers. This difference may be important because senders
and receivers play different roles in a communication transaction. The sender
may want to accomplish mutual understanding, but the receiver nay not want to
-14-
i
be bothered. The sender may have a higher stake in achieving mutual learning
than does the receiver. Previous research has not addressed this issue. But
(
it seems reasonable to assume that senders want to make sure the message gets
through, and will try to influence the receiver to have the same perspective
as held by the sender. The receiver, however, may want to resist being
i
influenced, and may simply want to receive the communication in the most
efficient fashion. Senders may prefer richer media because they want the
message to have more impact. Receivers may prefer less rich media so they
receive only the essential message, are less likely to be influenced, and have
more time to provide feedback. We hypothesize that sender-receiver status
will influence media selection.
Hypothesis 3: Managers in the position of information
sender will, on the average, select richer media for
communication transactions than will managers in the
position of information receiver.
Summary
This paper began with the dialectic between information engineers and
students of information behavior. Hypotheses about the relationship between
media selection and the translation requirements of communication episodes
were then developed. The trail of logic began with the premise that
managerial learning i3 a driving force underlying information behavior.
Communication episodes differ in the amount of learning required to achieve
mutual understanding, because of differences in perspective between sender and
receiver and the extent to which messages are equivocal and difficult to
interpret. The concept of translation was defined to reflect the amount of
mutual learning required in a communication transaction. The concept of media
richness was then introduced. We argued, based on an extension of
Bodensteiner ' s work, that media vary in the capacity to process information
and facilitate learning between managers. We concluded with a model that
-15-
proposed a positive relationship between media richness and translation
requirements as a way to test the validity of these ideas. Diverse findings
from the literature support the model, but manager personality and
sender/receiver position may moderate observed media selection behavior.
Research Method
The model described above is an extrapolation from the literatures on
organizational communications and managerial behavior. But the research
literature did not provide a basis for operationalizing and testing the model
Very little has been reported about the message content of managerial
communications or the role of specific media. This information had to be
generated as part of the overall study. The research to test the model
entailed a program of four projects. The first three projects developed
necessary instruments and an operational base for the fourth project, which
was the test of the Figure 2 richness model. The four projects were:
1 . Open-ended pilot study to ground the theory in the real world of
managerial communications.
2. Translation requirement study to identify a set of organizational
communication incidents representing a range of learning requirements.
3. Media hierarchy study to assess whether the ordering of media along
richness continuum is a logical assumption.
4. Final study to test the research model and to assess the moderating
influence of extrovert-introvert personality characteristics and
sender-receiver position ir. the communication transaction.
The remainder of this section describes the procedures used in these
studies, and reveals the learning process we went through while surmounting
the unknowns associated with operationalizing the concepts to test the model.
-16-
Pllot Study
The pilot study included open-ended, in-depth interviews with a
convenience sample of four practicing managers in three organizations. Three
of the subjects had general management responsibilities: one was president of
a bank; two were plant managers for manufacturing companies. The fourth
subject was the director of personnel for one of the manufacturers.
Each interview lasted three hours over two sessions. The interviews were
structured around the Critical Success Factor (CSF) technique (Rockhart, 1979,
1982). Managers were asked to identify key areas of responsibility and
performance, called CSF's. The CSF provided a concrete referent in the
manager's experience about which we could then identify information needs and
the communication activities associated with meeting those needs. The
interviews were tape-recorded and studied in detail. The goal was to learn as
much as possible about communication incidents and media used by managers and
to uncover problems or contingencies that would violate or strengthen the
richness model.
One outcome from this stage of research was identification of an expanded
list of communication media. Managers occasionally used media such as two-way
radios, telexes, and public address systems, although these media tend to be
peripheral to the manager's job. We also learned that managers did not think
in term3 of addressed and unaddressed documents. Memos, notes, and letters
are the organizational analogs of addressed documents. Fliers/bulletins, and
standard documents/reports are the analogs for unaddressed documents.
At the end of each interview, the model was presented to the managers to
solicit their comments or suggestions. Each manager understood and supported
the basic concept of the richness model. The managers did note, however, that
organizational circumstances might dictate the medium in specific situations.
They also agreed that personality may influence media preferences, and
ft
*
ft
>
»
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I
-17-
commented that while they would choose one medium to send a certain message,
they might prefer to receive the same message via a different medium.
Translation Requirements
Media identified in the pilot study were used to generate a sample of
communication incidents. The source of these data were interviews with eleven
practicing managers in eight organizations. These managers were also a
convenience sample, chosen to provide variation in hierarchical level,
functional responsibility and type of organization. The interview procedure
asked managers to discuss critical incidents in which they used each medium.
This method is the critical incident technique developed by Rosenbloom and
Wolik (1970) and subsequently employed by Dewhirst 0971). This technique
minimizes recall distortion by focusing on a concrete incident. Each manager
was first asked to recall the most "recent" use of a specific medium, and to
describe the content and purpose of the communication. Each manager was then
asked to recall a second, "important" use of the medium. Managers were also
asked open-ended questions about the reasons they choose that specific medium
for each communication. The overall objective of this interview process was
to refine our understanding of the purpose and content of specific managerial
communications.
These interviews generated 220 concrete examples of managerial
communications. Since these examples contained repetition and overlap, it was
possible to reduce the list to 60 incidents that were representative of
managerial communications. The incidents were selected based on the
specificity of the description and the probable generalizability to other
managers. However, there is no claim that the 60 incidents are a complete
representation of managerial communications. Rather these incidents represent
a broad cross section of communications that are grounded in actual managerial
work. The 60 incidents are listed in Appendix I.
-18-
Once the 60 communication incidents were developed, the amount of
translation required to achieve mutual understanding between sender and
receiver had to be identified. Translation scores for the incidents were
obtained from a panel who were asked to rate each of the 60 incidents. The
panel was composed of 17 management faculty members and 13 practicing managers
for a total panel of 30 judges. The translation concept was explained to each
judge and a written definition of the translation concept was provided. The
60 incidents were then rated on a five-point Likert scale. The average
translation rating for the 30 judges for each communication incident is
reported in Appendix I. A score above 4 represents a communication in which
the content or frames of reference would require extensive translation to
achieve mutual understanding. Translation scores below 2 are communications
for which mutual understanding is easy to achieve and little learning is
involved.
Media Richness
The next research project was to obtain an external validation for the
notion of a richness hierarchy. Once again, the judgments of an outside panel
were used. This panel consisted of 12 faculty members and 10 practicing
managers for a total panel of 22 judges. Each panel member was given a
written description of media richness and was asked to rate each medium on a
100 point scale (0 = lowest in richness, 100 = highest in richness).
The purpose of these data was to test whether an objective panel would
confirm our ordering of media along a richness hierarchy in descending order
from face-to-face, telephone, addressed documents, and unaddressed documents.
The media contained in each category of our original hierarchy are listed in
Table 1 along with the richness ratings and standard deviations. To test
whether the judgments of the panel supported the perception of a richness
hierarchy, t-tests for differences between ratings were calculated. The data
in Table 1 indicate that the judges' ratings are consistent with the hierarchy
of media richness. All judges perceived face-to-face as being highest in
richness, which is reflected in the score of 100. The telephone medium is
second, with a score of 85.9. Next in order are the letter (67.1), note
(64.4), and formal memo (54.1). The lowest richness ratings were given to
standard reports (32.3) and flier/bulletins (16.6), which are unaddressed
documents.
[Table 1 About Here]
The t-tests also support the original four richness classifications of
media as face-to-face, telephone, addressed documents and unaddressed
documents. The statistical significance between categories is greater than
the statistical significance among media within the same category. The
ratings of the external judges thus provide initial, external support for our
attempt to order media into a richness hierarchy.
The Model
Media selection. The primary hypothesis from the Figure 2 model is that
media richness will be associated with the translation requirements of
communication transactions. The method used for the final study was to
combine incidents and media into a single instrument, and to survey a new
sample of practicing managers about their communication preferences.
The new instrument contained all 60 incidents in Appendix I. Respondents
were given instructions for completing the instrument. A sample of 10 media
were provided for each incident, and each respondent was asked to select the
medium through which he/3he would prefer to send the message. The
instructions to respondents and the first incident on the questionnaire is
presented on the following page.
-20-
11. The exercize which foliage involves a aeries of cormunicaticn
incidents. Assume you ore sending a message in each case.
From the ten media classes defined on the previous page,
select the medium that you would use to accomplish each com¬
munication. You will need to refer to the media definitions
periodically during the exercise. When you have selected a
medium, indicate your choice by marking an "X” in the appro¬
priate box. If you choose a medium that does not clearly fit
one of the given categories, write your selection in the box
labeled "other. "
You are faced with the following communication tasks. Select the
medium you would use in each case by marking an "X" in the appro¬
priate box.
The purpose of the Communication Task is:
1. To give your immediate
subordinate a set of
five cost figures that
he requested last week.
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Ten media were used for response categories to provide a broad selection of
alternatives and to camouflage the underlying model. The final data analysis
included only the media that were included in the original model. The other
media — telex, special reports, public address — were seldom selected because
they are not part of typical managerial information processing.
Senders vs. Receivers. One moderating influence on media selection was
hypothesized to be sender vs. receiver orientation. The 60 incidents were
rewritten in a mirror image to reflect the receiver's perspective. For
example, the first incident was rewritten as follows. » <
To receive
immediate s
X ,
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message from your
~>cr\cr giving you
: cost figures
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-21-
>
One complete instrument was thus developed for the sender's perspective
which contained 60 incidents. Another complete instrument was developed
containing 60 incidents for the receiver's perspective. Each instrument
contained instructions to the respondent describing their role as sender or
receiver and asking them to check the media they would prefer for each
communication transaction.
Extrovert-Introvert . The final hypothesis pertained to personality as a
moderating variable in media selection. The instrument chosen to measure t
introversion-extroversion had to be short and relevant to mature, practicing
managers. The media selection exercise alone required a significant amount of
the respondents' time. The extrovert-introvert subscale of the Myers-Briggs ^
type indicator (Myers, 1962) was chosen. The subscale was extracted from the
full instrument, and provided 15 items that could be completed in about 5
minutes and had relevance to a mature audience. The extrovert-introvert »
subscale of the Myers-Briggs type indicator has been extensively validated for
its association with predicted behavioral differences (Carskadon, 1979;
Carlson and Levy, 1973). The questions came near the end of the questionnaire »
ju3t before the biographical information. Appendix II contains the
Myers-Briggs 3ubscale and the instructions to respondents. Coefficient alpha
for our respondents was .80, indicating acceptable internal reliability for *
the 15 items.
Sample . The principle criterion for selecting respondents to complete
the final instrument was that they be practicing managers with experience *
consistent with the communication incidents. The sample of managers was
obtained from a large (35,000 employee) petro-chemical corporation in Houston,
Texas. The initial sample was 109 managers from three divisions of the *
corporation. The sample was not random. The personnel department would not
give us access to the personnel files. The personnel manager drew the sample
»
-22-
based on a number of criteria, including the managers' availability during the
time of the study, at least one year on the job, and our request to obtain
representative responses from diverse functions and levels within the company.
The response rate was 87 percent, which yielded a final sample of 95 managers.
The sender version of the 60 communication incidents was completed by 46
managers, and 49 other managers completed the receiver version. All 95
managers completed the Myers-Briggs subscale. Since each manager responded to
60 communication episodes, the total possible sample for analysis wa3 5,700
incidents for which a medium was selected for a communication incident. This
was reduced by 204 for omitted or illegible responses, or for media checked
that were not part of the model.
Data Analysis. The question for data analysis was whether to test the
hypotheses with correlation and regression techniques based on absolute
numerical values from the judges' ratings, or to use simpler techniques that
utilized general categories. For example, a communication incident rated 4.1
on the translation scale was probably higher than an incident rated 2.3, but
it was not certain that the numbers represented the true translation values or
that the ratings constituted an interval scale. Since this was an exploratory
3tudy, we decided against premature rationalization of the data. Initial
analyses indicated that straightforward techniques of cross-tabulations,
means, percentages, and graphs fully revealed the underlying relationships.
With these methods we could test hypotheses while staying close to the
operational base of the research. Media thus were grouped into the four
categories of face-to-face, telephone, addressed documents, and unaddressed
documents for analysis. Communication incidents were grouped into four
categories representing low to high translation requirements.
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-23-
Research Findings
The central hypothesis in the richness model is that communication
translation requirements will be positively related to the richness of media
selected. The data pertaining to this hypothesis are shown in Table 2. Table
2 reports a cross-tabulation of the four media categories by four levels of
translation requirements. Visual inspection of Table 2 reveals a well defined
relationship between media richness and translation requirements. As the
translation requirement in a communication transaction increases, the
preference for richer media increases as predicted. For communication
transactions falling in the low translation category, only 13.5 percent of the
respondents preferred the face-to-face medium. This percentage increases to
84.1 percent when message translation requirements are high. By contrast,
62.4 percent of the respondents preferred a written, addressed medium for low
translation messages, but only 10.8 percent selected this medium for high
translation messages. A Chi-Square test of independence between translation
requirements and media richness was rejected at the .00001 level, which
indicates support for hypothesis 1. The Gamma coefficient for Table 2 is .56.
Gamma represents strength of association for ordinal variables in a
contingency table, and is similar in interpretation to a Spearman rank-order
correlation coefficient (Blalock, 1972; Nie, Hull, Jenkins, Steinbrenner and
Bent, 1975).
[Table 2 about here]
The media categories are combined into written and oral media to test
hypothesis la. These data are reported in Figure 3, which shows strong visual
support for the relationship between media and translation requirements. For
low translation transactions, 32.1 percent of respondents preferred oral
media. The preference for oral media Increased to 88.7 percent for
communications that have a high translation requirement. It appears that the
• «
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» I
» 4
» (
»
»
»
preference for rich media are stronger for high translation communications.
These data provide empirical support for the hypothesis that oral media are
preferred when translation requirements are high. For low translation tasks,
managers report a preference for written media.
[Figure 3 about here]
Unexpected Finding. Visual inspection of Table 2 suggests an additional
finding that was not hypothesized. The data in the right hand (high
translation) column are skewed toward the face-to-face medium (84.1 percent).
Moving to the left across Table 1, however, the distribution among media in
each column becomes broader. For translation requirements in column 2, for
example, 40.5 percent of the managers selected face-to-face, and 40.5 percent
selected an addressed document. The variation among media appears greater for
the simpler, low translation communications. This difference was tested by
calculating separate Chi-square and Gammas for the right half and left half of
Table 1. The Chi-square for the right half (third and fourth columns) is
105.8 (p < .00001), and the Gamma is .56, which indicate lack of independence.
The Chi-square for the left half of Table 1 is 71.8 (p < .0005) and the Gamma
is .44. This relationship is also statistically significant, but less so.
The significance test for the difference between Gammas is .02, which supports
the interpretation of a stronger relationship at higher levels of media
richness.
While this finding is tentative, it suggests a "convergence effect" by
managers toward rich media when translation requirements are high. Although
this convergence was not hypothesized, it does make sense in terms of the
underlying theory. The premise was that rich media are required to accomplish
high translation communications. Low rich media cannot process complex
messages or resolve different frames of reference, and therefore cannot
substitute for rich media when the learning requirement is high. On the other
hand, high rich media have more than sufficient capacity to process low
translation messages. The rich medium may not be efficient, but can
nevertheless serve as a substitute for low rich media in simple
communications. Thus managers have greater freedom to select across media
categories when routine information is conveyed.
Moderating Effects. Hypotheses 2 and 3 concern the extent to which
extrovert-introvert personality characteristics and sender-receiver roles
influence media selection. Table 3 shows the average media richness
preference for extroverts (82.2), introverts (81.5), senders (83.6), and
receivers (81.1). These scores represent the average media richness selected
for all 60 communication incidents. The differences in absolute scores are
quite small, but they are statistically significant. The difference between
introverts and extroverts is significant at the 0.06 level, indicating that
extroverts do prefer somewhat richer media on average than introverts.
Likewise, senders prefer somewhat richer media than receivers, which is
statistically significant at the 0.006 level. The findings in Table 3 suggest
modest support for hypotheses 2 and 3.
[Table 3 about here]
The important question about extrovert-introvert characteristics or
sender-receiver roles is whether these factors influence the underlying
relationship between translation requirements and media selection. Table 4
shows a contingency table breakdown of introverts vs. extroverts. Visual
inspection of the table shows that the percentages within respective
categories are similar to the percentages in the Table 1 categories. While
extroverts prefer slightly richer media on the average, this preference does
not effect the overall relationship between translation requirements and media
selection. The relationship between translation and media is illustrated by
the Chi-squares of 680 and 427 for Table 4, which are both statistically
significant at the .00001 level. Moreover, the zero-order Gamma between
translation and media is .536, and the first order partial Gamma controlling
for extrovert-introvert is .538, which indicates that the difference between
contingency tables is not significant.
[Table <4 about here]
Table 5 shows the breakdown of relationships by senders vs.
receivers. The percentages in respective cells are similar to Table i and to
each other. The preference of senders for slightly richer media does not
influence the underlying relationship between translation requirements and
media selection. The Chi-square tests for senders and receivers are both
statistically significant (.00001). The zero-order (.536) and first order
partial Gammas (.537) for Table 5 indicate no significant effect of
sender-receiver role on the relationship between media richness and message
translation requirements.
[Table 5 about here]
Finally, the impact of sender, receiver, extrovert, and introvert
(S-R-E-I) status on the selection of oral vs. written media are summarized in
Figure 4. The strength of the relationship between translation requirements
and media selection is revealed in the visual comparison of the S-R-E-I groups
in Figure 4. For all but the lowest translation category, senders show a
slightly higher preference for oral media than receivers, and extroverts show
a preference for oral media slightly greater than introverts. But these
relationships are secondary to the obvious increase in preference for oral
media with increasing translation requirements from the left to right side of
Figure 4.
[Figure 4 about here]
The data presented in thi3 section thus support the hypothesis that
communications with high translation requirements are associated with rich
media and low translation requirements are associated with media low in
richness. The hypothesis that oral vs. written media would follow the same
pattern was supported. The hypotheses that senders prefer richer media than
receivers and that extroverts prefer richer media than introverts received
modest support. However, these moderate relationships did not offset the
tendency across managers to select media based upon translation requirements.
Interpretation and Conclusions
The purpose of this research was to propose and test a theory to better
understand managerial information processing behavior. We proposed that
learning was an underlying force in information behavior, and that media are
chosen by managers based on the media's capacity to facilitate learning.
Four projects were undertaken to operationalize the richness model. The
results from the studies are summarized as follows: (1) The organization of
media into a richness hierarchy received external support from a panel of 22
judges. (2) A list of incidents representing a cross section of managerial
communications was developed, and the learning requirement of each incident
was identified by 30 judges. (3) The final sample of 95 managers provided
evidence to support a positive relationship between translation requirements
and media richness. (4) No matter how the responses were grouped — extrovert,
introvert, sender, receiver — the data demonstrated similarities in media
preferences based upon the nature of the translation requirements. Rich media
were consistently preferred when translation requirements were high. Media
low in richness tended to be preferred when translation requirements were low.
(5) An unexpected finding was that high translation communications seemed to
necessitate a rich medium, but managers could use a variety of media for the
low translation communications. (6) Differences in the media preferences for
senders, receivers, extroverts and introverts superimposed a small secondary
effect on the primary patterns.
Overall, the data provided support for the richness model, but the
findings must be interpreted within the limitations of the research. This was
an exploratory research program wherein concepts were operationalized for the
first time. Moreover a number of other variables could affect media
selection, such as physical accessibility (Huber, 1982), time and workload
constraints (Huber, 1982), perceived quality and reliability of sources
(O’Reilly, 1982), location in a communication network (Tushman, 1979), the
symbolic value of media (Feldman and March, 1981), and opportunity for
distortion (O'Reilly and Roberts, 1974). Further research is needed to assess
the validity of the media and translation concepts and to determine the
relationship of media selection to additional factors. The appropriate
conclusion at this point is to say only that the data have not disconfirmed
the richness model or the underlying theoretical explanation.
Organizational Information Processing
What do these findings mean for information processing in organizations?
We believe that the richness model provides a theoretical rationale for
interpreting some of the puzzlements in the research literature. For example,
why do managers prefer oral media and live action over written communications
and formal reports (Mintzberg, 1972)? Our findings suggest that the managers
observed in previous research probably were dealing with high translation
communications. Oral communications are richer than written communications.
Oral media are a better source of understanding for equivocal, ill-defined
issues. For example, Mintzberg observed top managers, who had to resolve
different perspectives and process subjective issues, hence they relied
heavily on rich media, including tours, the telephone, and face-to-face
meetings.
The managers in our study selected media both low and high in richness.
Indeed, they displayed a preference for notes, memos, and standard documents
for simple communication transactions that involved little learning. These
media are more efficient, and probably more suitable to the task. Managers
thus preferred both written and oral media, depending on the nature of the
communication transaction. The emphasis given to oral media in the literature
may be somewhat one-sided, based upon observations of managers who were
occupied with high translation communication tasks.
Next, why do managers presumably discount or even ignore management
information and decision support systems (Mitroff and Mason, 1983)? Our data
suggest two answers: (1) managers may use these unaddressed documents more
than we realize, and (2) formal information systems are not well suited to
high learning transactions. Information and decision support systems are in
all likelihood used for transmitting routine, objective, and impersonal
information that can be used throughout the organization. Managers can use
these sources for routine scanning, monitoring and control data about issues
that are well-defined and agreed upon, such as production volume. However,
standard documents do not substitute for a high rich medium. These documents
do not have the capacity for communications that require learning through
feedback, multiple cues, personal circumstances, and high variety language.
The failure of formal information and decision support systems (Ackoff, 1976;
Leavitt, 1975) is probably associated with their inappropriate application to
subjective and uncertain problems about which disagreement exists. Thus
formal information systems should not be viewed as failures. Rather their
success i3 contingent upon application to low translation communications, of
which there are many in organizations. Low rich media probably are more
efficient than face-to-face for relaying information about routine matters.
On the other hand, low rich media do not have the capacity or characteristics
to help managers resolve high translation issues.
Finally, why do formal scanning systems tend to filter out change signals
and promote programmed behavior within organizations (Hedberg and Jonsson,
1978)? The implication from this research is that a rich medium, especially
face-to-face, facilitates learning about issues characterized by diversity and
subjectivity. If this interpretation is generalized to organizational
learning, it says something about how organizations can diagnose their
environments. The formal structure of organization is represented in its
rules, formal scanning and information systems, budgets, performance
evaluation systems, and control systems. These characteristics often
represent low rich media that convey objective information through the
organization. Following this logic, formal management systems provide an
organization with low learning capabilities that are appropriate in a stable
environment (Huber, 1982).
When environments are complex and unstable, however, a role for rich
media emerges. Management can superimpose a less formal information structure
over the formal systems (Argyris, 1976). Managers themselves are responsible
for organization learning (Hedberg, 1981). Human beings are the key
communication medium. Technology based scanning systems do not substitute for
personal contacts, feedback, and high variety language. Managers can be in
personal touch with individuals and events in the external environment
(Aguilar, 1967; Keegan, 1974), and personally convey these ideas and
observations to others within the organization. The interpretation of
equivocal events requires rapid communication cycles among managers to define
rules and parameters (Weick, 1979). Rich media have the capacity for rapid
feedback so that convergence among managers is reached. Through face-to-face
discussions, environmental change can be interpreted and equivocality reduced
to the point where organizations can take appropriate action. Thus, managers
need to utilize rich media for organizational scanning when external events
-31-
are unstable and poorly defined.
To an objective observer, managerial work may appear to be disorganized
and fragmented. Managers seek live action and do not seem to be in control of
their time. These surface observations can be explained at a deeper level by
characterizing managers as information processors. Managers are attracted to
rich information through which they can interpret subjective issues and learn
about changing, complex environments. Managerial behavior and organic
organization structures enable the use of rich media for learning, adaptation,
and change. The richness model provides an information-based explanation for
managerial behavior and the role of organic processes in organizational
learning.
One path of new research to test these ideas would be to compare
managerial effectiveness with the selection of information media. Information
processing makes up a large part of the manager’s job, so selecting the right
medium for each communication may determine information quality, shared
understanding, and managerial effectiveness. Indeed, the richness model
suggests several streams of new research, including the laboratory testing of
media capacity, the classification of additional media, and the systematic
analysis of how characteristics (feedback, multiple cues, etc.) of each medium
influence information processing. Media selection may also be important to
research on larger organization processes, such as environmental scanning,
structure, and interdepartmental coordination.
In closing, we want to address once again the dialectic between
information engineers and students of information behavior that motivated this
research (Feldman and March, 1981). The findings about learning requirements
and media selection do not resolve the dialectic, but they do suggest a simple
idea for integrating these two perspectives. Communications within
organizations contain different learning requirements that influence the
richness of the medium selected. Information engineers have been concerned
with media low in richness that are appropriate for the efficient
communication of objective, impersonal data through the organization.
Students of information processing have focused on the use of rich media for
the resolution of personal, complex, subjective issues among managers. The
important point is that both kinds of issues exist within organizations, and
that both types of media are important. One view cannot be supported to the
exclusion of the other. The richness hierarchy provides a tentative way to
incorporate both viewpoints within the domain of organizational information
processing.
-33-
»
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Higgins, J. C., and R. Finn
1977 "The chief executive and his Information system". Omega, 5: 557-566.
Holland, W. E., B. A. Stead, and R. C. Leibrock
1976 "Information channel/source selection as a correlate of technical
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Huber, G.
1982 "Organizational information systems: Determinants of their performance
and behavior." Management Science, 28: 138-155.
Hunsaker, P.
1975 "Incongruity adaptation capability and risk preference in turbulent
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1974 "Multinational scanning: a study of the information sources utilized by
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1977 "The evolving concept of optimality." TIMS Studies in the Management
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Kefalas, A. G.
1974 "Environmental management information systems (ENVMIS): A
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1983 "Mintzberg was right! A replication and extension of The Nature of
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Larson, H. P.
1974 "EDP - A twenty-year ripoff." Infosystems, 21: November, 26-30.
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1967 "Differentiation and integration in complex organizations."
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Leavitt, H. J.
1975 "Beyond the analytic manager: I." California Management Review, 17, 3
5-12.
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1983 "Managerial information processing and communication-media source
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Mackay, D.
1969 Information, Mechanism and Meaning. Cambridge, Mass.: MIT Press.
Martin, J., and M. E. Powers
1980b "Skepticism and the true believer: the effects of case and/or baserate
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1980a "Truth or corporate propaganda: the value of a good war story." In L.
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1972 "The how and what of why: Some determinants and consequences of causal
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1976 "The lesser influence of consensus than distinctiveness information on
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1971 Silent Messages. Belmont, Ca.: Wadsworth.
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1969 Technology and the Worker. San Francisco: Chandler.
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1972 The myths of MIS. California Management Review, 15, 1: 92-97.
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»
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1980 Human Interference: Strategies and Shortcomings of Social Judgement.
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1980 "Individual and information overload in organization: Is more >
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1974 "Information filtration in organizations: three experiments."
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1983 "Information technology: a new competitive weapon." Sloan Management
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1976 "Communication in organizations." In M, P. Dunvette (ed.), Handbook of
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1978 "Organization technology and the media and purpose dimensions of
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1977 "Computers and management structure: Some empirical findings
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Rockhart, J. F.
1979 "Chief executives define their own data needs." Harvard Business
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1982 "The changing role of the information systems executive: A critical
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1970 Technology and Information Transfer: A Survey of Practice in Industrial
Organizations. Boston: Harvard University, Graduate School of Business
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1949 The Mathematical Theory of Communication. Urbana, Ill.: University of
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Shrivastava, P., and I. I. Mitroff
1984 "Enhancing organizational research utilization: the role of decision
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Stabell, C. B.
1978 "Integrative complexity of information environment perception and
information use." Organizational Behavior and Human Performance, 22:
116-142.
Tushman, M. L.
1979 "Managing communication networks in R&D laboratories." Sloan Management
Review, 20, 2:37-49.
Tushman, M. L., and D. A. Nadler
1973 "Information processing as an integrating concept in organizational
design." Academy of Management Review, 3: 613-624.
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1976 "Determinants of coordination modes within organizations." American
Sociological Review, 41: 322-338.
Weick, K. E.
1979 The Social Psychology of Organizing. Reading, MA: Addison-Wesley ,
1979.
Increasing
Media
Richness
Media Characteristics
Classification
Feedback
Channels &
Cues
Source
Language
Face-to-face
Oral
Immediate
Audio &
Visual
Personal
Natural
Telephone
Oral
Fast
Audio
Personal
Natural
Addressed
Documents
(e.g., let¬
ters, Memos)
Written
Slow
Limited
Visual
Less
Personal
Natural
Jnaddressed
Documents
(e.g. MIS
Reports, News
letters)
Written
Slowest
Limited
Visual
Impersonal
Numeric
or
Natural
high
INFORMATION
RICHNESS
low
low high
MESSAGE TRANSLATION
(LEARNING) REQUIREMENT
Figure 2. Proposed Model of Managerial Information Processing
Table 1: Media Richness Ratings
Media
Media Richness
Rating
t-test for differences
between media richness ratings
Mean
(s .d. )
t-value
Probability
Face-to-Face
100.00
(0.00) —
1
Telephone
85.86
(7.0) —
} 9.5
~l
.0001
Addressed Documents
) 6.6
.0001
Letter
67.14
(15.3)—
10 8
AAQ
Note
64.36
(18.5) —
l 1 7
i ns
Formal Memo
54.05
(19.9) —
> 1 ■ '
Unaddressed Documents
l 4.25
.0001
Standard Report
32.3
(23.4) —
-J
l ? ^
OS
Flier /Bullet in
16.6
08.3) —
(N - 22 Judges)
Table 2: Relationship Between Message Translation Requirement and
Preferred Media Richness _
Low -<-■
Translation
Requirement
-> High
Information Medium
1 *, ±2
percent (N)
2 Z, 4-3
percent (N)
3 4. , £- 4
percent (N) 1
percent (N)
Face-to-l'ace
13.5
(148)
40.5
(598) |
(
60.6
H1
CO
ro
84.1
(546)
Telephone
18.6
(203)
18.3
(271)
9.4
.
(208)
4.6
(30)
Addressed Documents
62.4
(683)
40.5
(598)
00
(628)
10.8
(70)
Unaddressed Documents
5.5
(60)
0.7
(ID
1.7
(37)
0.5
(3)
100
(1098)
100
(1478)
100
(2215)
100
(649)
(x‘ = 1099.13; significance = .00001)
(Gamma = . 54 )
<
<
(
Table A: Relationship Between Translation Requirement and Media Richness, by Extrovert /Introvert
fable 5: Relationship between Translation Requirement end Media Richness, by Sender/Receiver
APPENDIX I
Sample of Communication Incidents
Derived from Critical Incidents
Translation Score
The
Purpose of the Communication was:
Mean
S.D.
1.
To give your immediate subordinate a set of fiva cost figures that
he requested last week.
1.74
1 . Ub
2.
To present some confusing changes in the employee benefit
package to 20 subordinates.
4. DO
1.U2
3.
To get an explanation or clarification of the conclusions in a
statistical study done by an tr-house consulting group.
3.72
1.00
4.
To convince your immediate superior that you need to increase
your manpower to complete an important project on schedule.
3.51
1 .06
5.
To find out if an immediate subordinate has been accurately
reporting progress on a very important project.
4.2
1 .20
6.
To give an easy-to-understand. routine assignment to an immediate
subordinate who has an abrasive personality.
2.2o
. v
7.
To get basic information from your immediate superior that is
needed to set up an itinerary for a two-day management meeting
to be chaired by this superior.
4.4b
1 .u
8.
To direct a subordinate (two levels below you) to handle a routine
problem wuh a cross-town client.
2.25
1 . u2
3.
To remind a subordinate (two levels below you) that she is
scheduled to attend a mealing on Friday at 3:00 p.m.
1.34
. CD
10.
To notify an immediate subordinate that his request for a leave of
absence has been approved.
1.37
.50
1 1.
To notify five subordinates that you have to cancel a meeting with
them tomorrow, but that you can make it at the same time the
following day if they can.
1 . 02
.ol
12.
To delegate a routine paperwork chore to an Immediate
subordinate.
1.^0
.30
12.
To express your dissatisfaction with the way your office is being
c'eaned to the janitorial staff.
2 . oU
.DO
14.
To notify your 20 subordinates about a new stacgered-hour
wcxing schedule going into effect at the end of the month.
3 . G ! j
Translation Score
j
i
Mean
• S.D.
15.
To work out a personality problem occurring between your
immediate subordinate and one of his subordinates.
4.11
. o2
Ifl.
To reprimand an immediate subordinate for missing a deadline on a
minor project.
2. CO
1.12
17.
To give an easy-to-understand. routine assignment to an immediate
subordinate who is a personal friend.
1 . bO
. 7u
18.
To remind a superior that she is scheduled to attend a meeting
with your work group on Friday at 3:00 p.m.
1 . od
.63
19.
To 'tell your subordinates that your firm has lost a major contract
and that this could affect their employment status.
4 . 2U
.82
20.
To get the opinion of a trusted peer about how to deal with an
unusual problem you are facing.
3.25
1.00
21.
To explain to a new, ( rather sensitive, employee that she
mishandled a personnel conflict in her work group.
4.20
.90
22.
To work out a personality problem that has affected the working
relationship between you and your boss.
4.40
.90
23.
To notify a subordinate (two levels below you) that he did not fill
out an expense report properly.
2.30
.93
.93
24.
To persuade one of your peers to stay with your firm and to turn
down an attractive job offer with another firm.
3.44
1.14
25.
To reprimand an immediate subordinate for missing a deadline on a
major project, thereby embarrassing you in front of your boss.
3.23
1.10
26.
To ask a peer to give a talk in your place at a Rotary Club
luncheon next week.
2.90
1.02
27.
To reassure your subordinates that their job security is net
threatened by the loss of a major contract.
3.44
1 .14
28.
To inform a trusted superior about the way you have chosen to
handle an unusual situation.
2.93
. 88
29.
To get an explanation from a subordinate, who is a personal
friend, about what appears to be a "padded" expense report.
3.70
1 no
30.
To work out the reQuirements for a new project with your boss.
3.50
1.23
1
I Translation Score j
I Mean S.D. !
31. To express your "official" appreciation to one of your immediate J
subordinates, who is ifsving the company after ten years of loyal ^
service. 1.86 1.12
32.
To get clarification of an ambiguous directive from your boss.
3.41
0.95
33.
To inform your 20 subordinates of the time and place of your
work unit's annual Christmas party.
1.30
.50
*
<
34.
To let a new worker know that he is doing an excellent job and
that you are p leased.
2.16
1.09
35.
To get your boss's reaction to your request for a cne-month leave
of absence for "personal business."
3.80
0.83
<
36.
To warn a "problem" subordinate that he better start showing up
for work on time.
3.21
1.24
37.
To explain to subordinates how important the project they are
working on will be to their careers.
i
3.41
0.98
4
38.
To request the presence of your boss at your work unit's
Christmas party.
1.60
0.80
39.
To get an ides of your boss's expectations for your group for the
next six months.
3.03
1.17
«
40.
To ask your subordinates for suggestions about the reorganization
of work and responsibilities in your group.
3.65
1.19
41.
To get an explanation from a subordinate, who is difficult to get
along with, about what appears to be a "padded” expense report.
3.95
0.92
i
42.
To work out confusing terminology used by a new subordinate
reporting progress on a routine work assignment.
3.67
0.77
43.
To get your boss's impression of an idea you had for handling
customers' complaints in the future.
3.11
0.90
i
44.
To explain a new, rather complicated policy change to a
subordinate who will be singularly affected by it.
3.95
0.95
45.
To remind an immediate subordinate about a task that should have
been completed yesterday.
2.02
0.93
«
46.
To get an explanation from a peer in another department of a
complicated technical matter in which you have little formal
training or experience.
4.25
0.75
1
Transaction Score
1
To warn a subordinate who is a former superior that he has taken
Mean
S.D.
47.
action beyond the bounds of his authority and that he is no longer
the boss.
3.90
1.00
48.
To suggest to a new employee that she is not doing an adequate
job and would be better off accepting a demotion to a less
demanding position. The alternative is dismissal.
4.41
0.73
49.
To get an explanation from a peer in another departmer' of a
complicated technical matter in which you have formal training and
experience.
2.90
0.83
50.
To warn a superior diplomatically that her arrogant and
authoritative behavior is affecting the morale of your group.
4.23
0.78
51.
To solicit suggestions from your subordinates for new ways to
market or package an old product.
2.36
1.03
52.
To work out confusing terminology used by an experienced
subordinate reporting progress on a major, non-routine project.
3.55
0.93
53.
To offer a recommendation to a peer for one of your friends,
who is epplying for a job in his group.
2.71
1.00
54.
To direct your secretary to order twice as many note pads this
month as she usually does.
1.41
0.93
55.
To explain to your new secretary how you want your phone calls
handled.
2.41
1.00
56.
To express displeasure to your superior about the careless,
error-filled reports you have been getting from a peer in another
work group.
3.58
0.80
57.
To let a peer know that, in your opinion, a woman he would like
to hire will not be able to handle the job.
3.35
0.90
58.
To notify an applicant for a position in you r group that she will
not be offered the job.
2.65
1.20
59.
To notify your five subordinates that the plan they worked out for
coordinating project assignments has been approved and will go
into effect next month.
1.83
1.C2
60.
To let a new employee know that you are monitoring his
performance and are pleased with his progress.
2.16
1.17
APPENDIX II
Part
MYERS-BRIGGS SHORT FORM
This exercise addresses various dimensions of your per¬
sonality that might be related to your communication media
preferences. There are no "right" or "wrong" answers to
these questions. Circle the response which most accurately
describes you. Do not think too long about any question.
A. Which answer comes closer to telling how you usually feel
or act?
1. Are you usually
a. a "good mixer", or
b. rather quiet and reserved?
2. - When you are with a group of people, would you usually
rather
a. join in the talk of the group, or
b. talk with one person at a time?
3. In a large group, do you more often
a. introduce others, or
b. get introduced?
4. Do you tend to have
a. deep friendships with a very few people, or
b. broad friendships with many different people?
5. Among your friends, are you
a. one of the last to hear what is going on, or
b. full of news about everybody?
6. Do you
a. talk easily to almost anyone for as long as you have
to, or
b. find a lot to say only to certain people or under
certain conditions?
7. Can the new people you meet tell what you are interested in
a. right away, or
b. only after they really get to know you?
LIST 1
MANDATORY
Defense Technical Information Center (12 copies)
ATTN: DTIC DDA-2
Selection and Preliminary Cataloging Section
Cameron Station
Alexandria, VA 22314
library of Congress
Science and Technology Division
Washington, D.C. 20540
Office of Naval Research (3 copies)
Code 4420E
800 N. Quincy Street
Arlington, VA 22217
Naval Research Laboratory (6 copies)
Code 2627
Washington, D.C. 20375
Office of Naval Research
Director, Technology Programs
Code 200
800 N. Quincy Street
Arlington, VA 22217
A 4, 20E
Dec 83
LIST 2
ONR FIELD
Psychologist
Office of Naval Research
Detachment, Pasadena
1030 East Creen Street
Pasadena, CA 91106
4420E
Dec 83
1. 1ST 3
OPNAV
Deputy Chief of Naval Operations
(Manpower, Personnel, and Training!
Hoad, Research, Development, and
Studies Branch (Op-115)
1812 Arlington Annex
Washington, DC 20350
Director
Civilian Personnel Division (OP-14)
Department of the Navy
1803 Arlington Annex
Washington, DC 20350
Deputy Chief of Naval Operations
(Manpower, Personnel, and Training)
Director, Human Resource Management
Plans and Policy Rranch (Op-150)
Department of the Navy
Washington, DC 20350
Chief of Naval Operations
Head, Manpower, Personnel, Training
and Reserves Team (Op-964D)
The Pentagon, 4A478
Washington, DC 20350
Chief of Naval Operations
Assistant, Personnel Logistics
Planning (Op-987H)
The Pentagon, 5D772
Washington, DC 20350
4420E
Dec 83
LIST 4
NAVMAT & NPRDC ,
NAVMAT
r
rrnjjr.m Administrator for Manpower,
Personnel, and Training
MAT-0722 ,
800 N. Ouincy Street
Alii up ton, VA 22217
Naval Material Command
Management Training Center
NAVMAT ( >9M32 (
Iif for son Plaza, Rldg II 2, Rm 150
1471 Jefferson Davis Highway
Arlington, VA 20360
Naval Material Command
Director, Productivity Management Office <
MAT-OOK
Crystal Plaza If 5
Room 632
Washington, DC 20360
t
Naval Material Command
Deputy Chief of Naval Material, MAT-03
Crystal Plaza If 5
Room 236
Washington, DC 20360
Naval Personnel R&D Center (4 copies')
To clinical Director
Director, Manpower 4 Personnel
laboratory , Code 06
Director, System Laboratory , Code 07
Director, Future Technology, Code 41
S.an Diego, CA 92152
Navy Personnel R&D Center
Washington Liaison Office
Rallston Tower I1 3, Room 93
A rl i ng ton , VA 222 1 7
44 20E
Dec 83
LIST 6
NAVAL ACADEMY AND NAVAL POSTGRADUATE SCHOOL
Naval Postgraduate School (3 copies)
ATTN: Chairman, Dept, of
Administrative Science
Department of Administrative Sciences
Monterey, CA 93940
U.S. Naval Academy
ATTN: Chairman, Department
of Leadership and I. aw
Stop 7-B
Annapolis, MD 21402
Super int cndent
ATTN: Director of Research
Naval Academy, U.S.
Annapolis, MD 21402
4420E
Dec 83
LIST 9
USMC
Headquarters, U.S. Marine Corps
Code MPI-20
Washington, DC 20380
Headquarters, U.S. Marine Corps
ATTN: Scientific Adviser,
Code RD-1
Washington, DC 20380
Education Advisor
Education Center (E031)
MCDEC
Quantico, VA 22134
Commanding Officer
Fducation Center (E031)
MCDEC
Quantico, VA 22134
Commanding Officer
U.S. Marine Corps
Command and Staff College
Quantico, VA 22134
4420F.
Dec 83
LIST 10
OTHER FEDERAL GOVERNMENT
Defense Advanced Research
Projects Agency
Director, Cybernetics
Technology Office
1400 Wilson Rlvd, Rib 625
Arlington, VA 22209
Dr. Douglas Hunter
Defense Intelligence School
Washington, DC 20374
Dr. Rrian I'silaner
GAO
Washington, DC 20548
National Institute of Education
K01.C/SM0
1200 19th Street, N.W.
Washington, DC 20208
National Institute of Mental Health
Division of Extramural Research Programs
5600 Fishers Lane
Rockville, MD 20852
National Institute of Mental Health
Minority Group Mental Health Programs
Room 7-102
5600 Fishers Lane
Rockville, MD 20852
Office of Personnel Management
Office of Planning and Evaluation
Research Management Division
1900 F. Street, N.W.
Washington, DC 20415
Chief, Psychological Research Branch
U.S. Coast Guard (C-P-l /2/TP42)
Washington, D.C. 20593
Em ial and Developmental Psychology
Program
N.O'onal Science Foundation
W. sh i ngt on , D.G. 20550
I1 r. r a r 1 Potter
U.S. Coast Guard Academy
New London, GT 06320
4420E
Dec 83
LIST 10 CONT'D
OTHER FEDERAL GOVERNMENT
Division of Industrial Science
& Technological Innovation
Productivity Improvement Research
National Science Foundation
Washington, D.C. 20550
Douglas B. Blackburn, Director
National Defense University
Mobilization Concepts Development
Center
Washington, D.C. 20319
Chairman, Dept, of Medical Psychology
School of Medicine
Uniformed Services University of
the Health Sciences
4301 Jones Bridge Road
Rethcsda, MD 20814
4420E
Dec 83
LIST 11
ARMY
Headqua rt ers , FORSCOM
ATTN: AFPR-HR
Ft. McPherson, CA 30330
Array Research Institute
Field Unit - Leavenworth
P.0. Box 3122
Fort Leavenwor th , XS 66027
Technical Director (3 copies)
Array Research Institute
5001 Eisenhower Avenue
Alexandria, VA 22333
Head, Department of Behavior
Science and Leadership
U.S. Military Academy, New York 10996
Valter Reed Army Medical Center
W. R. Army Institute of Research
Division of Neuropsychiatry
Forest Oten
Washington, D.C. 20012
Army Military Personnel Command
Attn: DAPC-OE
200 Stovall Street
Alexandria, VA 22322
Research Psychologist
Selection and Classification Performance
Measurement Team
Army Research Institute
Attention: PERI-RS
6001 Eisenhower Avenue
Alexandria, VA 22333
i
»
»
4420E
Pec 83
LIST 12
AIR FORCE
Air University Library
I.SF. 76-443
Maxwell AFB, AL 36112
Head, Department of Behavioral
Science and leadership
H.S. Air Force Academv, CO 80840
MAT Robert Oregory
HSAFA/DFRL
H.S. Air Force Academy, CO 80840
AFOSR/NL
Building 410
Bolling AFB
V’ashi ngt on , DC 20332
Department of the Air Force
HQHSAF/m’XHL
Dent agon
Washington, DC 20330
Technical Director
AFI1RL/M0 IT)
Brooks AFB
San Antonio, TX 78235
AFMPC/MI’CYFR
Randolph AFB, TX 78150
4420E
Dec 83
Sequential by Principal Investigator
LIST 14
CURRENT CONTRACTORS
Dr. Clayton P. Alderfer
Yale University
School of Organization and Management
New Haven, Connecticut 06520
Dr. Janet L. Ba rnes-Fa r rel 1
Department of Psychology
University of Hawaii
2430 Campus Road
Honolulu, HI 96822
Dr. Jonills Braddock
John Hopkins University
Center for the Social Organization
of Schools
3505 N. Charles Street
Ra 1 1 iniore , MD 21218
Dr. Jeanne M. Brett
Northwestern University
Graduate School of Management
2001 Sheridan Road
Fvnnston, IL 60201
Dr. Terry Connolly
Georgia Institute of Technology
School of Industrial & Systems
Engineering
Atlanta, CA 30332
Dr. Richard Daft
Texas ASM University
Department of Management
College Station, TX 77843
Dr. P.nndv Dunham
University of Wisconsin
Graduate School of Business
Mi 1 1 ">n , W I 6 3 706
List 14 ^continued)
Dr. Henry Fmurian
The Johns Hopkins University
School of Medicine
Department of Psychiatry and
Behavioral Science
Baltimore, MD 21205
Dr. Arthur Gerstenfeld
University Faculty Associates
710 Commonwealth Avenue
Newton, MA 02159
Dr. J. Richard Hackman
School of Organization
and Management
Box 1A, Yale University
New Haven, CT 06520
Dr. V.'ayne Holder
American Humane Association
P.0. Box 1266
Denver, CO 80201
Dr. Daniel Ilgen
Department of Psychology
Michigan State University
Fast Lansing, MI 48824
Dr. Lawrence R. James
School of Psychology
Georgia Institute of
Technology
Atlanta, GA 30332
Dr. David Johnson
Professor, Educational Psychology
178 Pillsbury Drive, S.E.
University of Minnesota
Minneapolis, MN 55455
Dr. F. Craig Johnson
Department of Educational
Resrach
Florida State University
Tallahassee, FI. 3 2 306
2
4420F.
Dec 83
m
list 14 (continued!
Dr. Dan Landis
Departnent of Psychology
Purdue University
Indianapolis, IN 46205
Dr. FranV J. Dandy
The Pennsylvania State University
[V[ a r t ncnt of Psychology
417 Pruce V. Moore Building
University Park, PA 16802
hr. Bibb Latane
71 *• University of North Carolina
at Chapel Hill
M inni ng Ha 1 1 026A
< ' .pel Hill, NC 27514
Dr. Fdward R. Lawler
''niversity of Southern California
Graduate School of Business
Administration
Los Angeles, CA 90007
Dr. Cynthia D. Fisher
College of Business Administration
Texas ASM University
College Station, TX 77843
Dr. Lynn Oppcnheira
Wharton Applied Research Center
University of Pennsylvania
Philadelphia, PA 19104
Dr. Thomas M. Ostrom
The Ohio State University
Department of Psychology
1 16F. Stadium
404C West 17tli Avenue
Columbus, OH 43210
Dr. William G. Ouchi
University of California,
Los Angeles
Graduate School of Management
l.os Angulos, CA 90024
3
4420E
Dec 83
list 14 (continued)
Dr. Hubert Rice
State University of New York at Buffalo
Department of Psychology
Buffalo, NY 14226
Dr. Trwin C. Sarason
University of Washington
Department of Psychology, Nl-25
Seattle, WA 98195
Dr. Benjamin Schneider
Department of Psychology
University of Maryland
College Park, MD 20742
Dr. Fdgar H. Schein
Massachusetts Institute of
Technology
Sloan School of Management
Cambridge, MA 02139
Dr. H. Wallace Sinaiko
Program Director, Manpower Research
and Advisory Services
Smithsonian Institution
801 N. Pitt Street, Suite 120
Alexandria, VA 22314
Dr. PI lot Smith
Purdue Research Foundation
llovde Hall of Administration
West l.afayette, IN 47907
Dr. Richard M. Steers
Craduate School of Management
University of Oregon
Eugene, OR 97403
Dr. Siegfried Streufert
The Pennsylvania State University
Department of Behavioral Science
Milton S. Hershey Medical Center
He r- hcv , PA 1 7033
Dr. Barbara Saboda
u 1 * 1 i ; Applied Systems Division
West ingltouse electric Corporation
P.0. Box 866
CulunMa, MD 21044
4
list 14 (continued)
Dr. Harry C. Trlandls
Department of Psychology
University of Illinois
Champaign, IL 61820
Dr. Anne S. Tsui
Duke University
The Fuqua School of Business
Durham, NC 27706
Dr. Andrew H. Van de Vcn
University of Minnesota
Office of Research Administration
1919 University Avenue
St. Paul, MN 55104
Dr. Phil ip Kexler
University of Rochester
Graduate School of Education &
Human Development
Rochester, NY 14627
Dr. Sabra Woolley
SRA Corporation
901 South Highland Street
Arlington, VA 22204
5
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